AI internal knowledge assistant
Instant, reliable access to internal knowledge.
Try our Operations Assistant in action. Open the demo.
IT, Finance, Legal, Leadership, and other team assistants are available on request.
Problem
Company knowledge is scattered across multiple systems, network drives, and emails. Employees spend hours searching or repeatedly ask colleagues the same questions. Onboarding new employees is slow because information must be gathered from multiple sources. Version control is unclear, and outdated documentation leads to misunderstandings and errors.
Solution
We build a RAG-based knowledge assistant that indexes all internal documents into a single FAISS vector store. The assistant searches semantically and responds based on context. Every answer includes source citations and quotes from original documents. If information isn't found, the system clearly responds "I don't know" instead of guessing. The admin panel manages documents and monitors usage. The system supports multiple LLM models (Ollama, OpenAI, Anthropic, Google) and integrates with existing systems.
How AI internal knowledge assistant helps
The internal knowledge assistant removes information search frustration and speeds up decision-making. When knowledge is scattered, employees spend hours searching and ask the same questions repeatedly — this solution provides instant answers with source citations.
The system improves onboarding experience and reduces dependency on colleagues. RAG technology ensures answers are always based on actual documents, not guesses. We use the same solution in our own internal knowledge management.
Key Benefits
Employees find information in seconds by asking in natural language. Onboarding time decreases significantly as new employees get instant answers. Teams receive fewer repetitive questions as the assistant handles them automatically. Decision-making speeds up when information is easily accessible. Source citations ensure reliability and transparency. Role-based access control ensures sensitive documents are visible only to authorized personnel.
Implementation Timeline
Implementation typically takes 2–6 weeks depending on document volume and integration needs. The first week is spent on document inventory, access control definition, and RAG architecture configuration. The second week is used for document indexing and test usage. The third week includes integration with existing systems and UI finalization. Remaining time is spent on user training, documentation, and fine-tuning. Production usage begins gradually with a pilot group.
Technical Architecture
The system uses the LangChain library and FAISS vector store for semantic search. Documents are chunked and indexed with an embedding model. User queries are searched in vector space, and relevant chunks are sent to the LLM as context. Responses are generated with source citations. Authentication is implemented with JWT tokens and role-based access control with a database. The system is container-based (Docker) and scalable.
Features
A RAG-based knowledge assistant for internal company use. Built as a customizable template for IT, finance, legal, operations, and leadership teams.
Key outcomes
Process
Discovery
Pilot
Integrations
Rollout
Optimization
Data & integrations
- CRM and support systems
- Documents and knowledge bases
- APIs and data sources
Security & compliance
- Processing designed for agreed privacy requirements
- Audit trail and logging
- Clear boundaries and access control
Use cases
Policies and procedures
Onboarding support
Document retrieval
FAQ
What is the difference between a regular search engine and a knowledge assistant?
A regular search engine returns links to documents based on keywords. A knowledge assistant understands the meaning of the question, finds relevant sections from multiple documents, and formulates an answer based on context. It cites sources and clearly states when information isn't found. This saves time and reduces misunderstandings.
How do we ensure answers are correct?
Every answer includes source citations to original documents and quotes from the sections used. Users can verify sources themselves. If the system doesn't find information, it responds "I don't know" instead of guessing. Audit logs record all queries and responses for quality assurance.
How long does document indexing take?
Indexing depends on document quantity and size. Typically 100-500 documents are indexed in a few hours. Larger volumes (thousands of documents) may take a day. Indexing is done once, and after that new documents are added continuously or on a scheduled basis.
Can the knowledge assistant handle multilingual documents?
Yes. The system supports multiple languages simultaneously. Embedding models and LLMs can process multilingual documents and respond in the user's chosen language. We can configure the system to support, for example, Finnish, English, and Swedish simultaneously.
How are access rights managed?
The system uses role-based access control (RBAC). Documents can be tagged for different roles (e.g., HR, IT, management), and users see only documents they have access to. The admin panel manages users, roles, and document permissions. All login attempts and queries are logged.
Can the knowledge assistant be integrated with existing systems?
Yes. We can integrate the knowledge assistant with network drives, wikis, Confluence, or other document repositories. The system can automatically fetch documents from these sources and keep the index up to date. The web chat widget is embeddable on any site.
What happens when documents are updated?
The system can be configured to re-index documents automatically on a scheduled basis (e.g., nightly, weekends) or in real-time when a document changes. Old versions can be kept as historical data or replaced with new ones. The admin panel shows when documents were last indexed.
How much does the knowledge assistant cost?
Costs depend on document volume, LLM model (local Ollama vs. cloud service), hosting environment, and integrations. A typical project includes: 1) Development and configuration (one-time), 2) Document indexing (one-time), 3) Hosting and LLM usage (monthly). We provide a customized quote based on your needs.
Let’s plan your service
Tell us your goals and process, we will propose a plan.
You can see our Operations Assistant demo here. Open the demo.
IT, Finance, Legal, Leadership, and other team assistants are available on request.
